Apparatus and process for monitor and control of an ammoxidation reactor with a fourier transform infrared spectrometer

ABSTRACT

The present invention is a method and an apparatus for identifying and quantifying components in an effluent stream from an ammoxidation reactor, the apparatus comprising a microprocessor; and a Fourier Transform infrared spectrometer having a sample cell through which may flow a portion of the effluent stream, an infrared source to emit infrared radiation and pass the infrared radiation through the effluent stream, an infrared detector to detect transmitted infrared radiation at the selected infrared wavelengths and to generate absorbance data due to absorbance of the infrared radiation by the components, wherein each of the components absorbs infrared radiation at one or more of the infrared wavelengths, and an output apparatus to provide the absorbance data to the microprocessor; wherein the microprocessor is programmed to identify and quantify each of the plurality of components based upon the absorbance data and calibration data, the calibration data being obtained from recovery run analyses and calibration analyses in the sample cell. The method may be applied to utilize the apparatus to provide real-time control of the operation of an ammoxidation reactor, based on the analytical results obtained by the FT-IR spectrometer and the calibration model developed therefor.

TECHNICAL FIELD OF THE INVENTION

[0001] The present invention relates to identification andquantification of a plurality of components in the effluent of anammoxidation reactor by means of Fourier Transform infrared (FT-IR)spectroscopy and use of the information thus obtained to provide controland optimization of the ammoxidation reaction.

BACKGROUND OF THE INVENTION

[0002] The present invention finds significant use in the ammoxidationof both propylene and propane to produce acrylonitrile, and in generalin the ammoxidation of olefins, paraffins and other starting materialsto produce the corresponding nitriles. This reaction is well known andis described, for example, in U.S. Pat. Nos. 3,642,930 (olefins) or4,897,504 (paraffins), the disclosures of which are incorporated hereinby reference. In general, the ammoxidation reaction is accomplished bycontacting the reactant olefin or paraffin (or other starting material),oxygen and ammonia, in the vapor phase, with a particular ammoxidationcatalyst, at an elevated temperature and at atmospheric or nearatmospheric pressure. The reaction may be carried out in the same mannerand under the conditions generally set forth, for example, in the '930patent or the '504 patent.

[0003] In addition to olefins and paraffins, oxygenated hydrocarbons canbe ammoxidized with the known ammoxidation catalysts. For example,alcohols such as isopropanol, n-propanol, t-butyl alcohol, and aldehydessuch as acrolein and methacrolein can be readily converted to nitriles.In general, the starting materials are olefins, paraffins, aldehydes andalcohols containing three or four carbon atoms. The general ammoxidationprocess for converting olefins, alcohols and aldehydes to thecorresponding nitriles is well known and described for example in U.S.Pat. Nos. 3,642,930 and 4,897,504, and others assigned to The StandardOil Company.

[0004] The following description of the ammoxidation reaction, both inthe background and in the description of the invention, may use anolefin, sometimes specifically propylene, for exemplary purposes. Theinvention is not so limited and is applicable to ammoxidation reactionsusing any known starting material and particularly including paraffinsin addition to olefins. It is further noted that, as would be understoodby a person of skill in the art, it may be necessary to adjust theprocess, including changing catalysts used, according to the particularstarting material employed and according to the products desired to beproduced. For convenience herein, the term “hydrocarbon” may be employedfor referring to the organic feed material, be it olefin, paraffin orother known ammoxidation feed material.

[0005] In monitoring and controlling the ammoxidation reaction, it hasheretofore been the practice in the industry to operate the reactorbased on test results obtained from previous operations of the reactor,where the test results are obtained from quality control proceduresknown as “recovery runs”. Recovery runs are laboratory chemical analysesperformed on collected samples of the effluent stream and/or collectedproducts of the ammoxidation reaction (i.e., a day's production).Recovery runs require a minimum of several hours to perform, so cannotprovide contemporaneous, real-time information as to the ammoxidationreaction. For these reasons, recovery runs can only provide hindsightinformation as to the parameters of operation of the ammoxidationreaction. The industry has long sought both more rapid analysis of thereaction products and a way to provide such information in real time, soas to allow the control and optimization of the ammoxidation reactionduring the course of a reaction, i.e., in “real-time”.

SUMMARY OF THE INVENTION

[0006] In one embodiment, the present invention is an apparatus foridentifying and quantifying components in an effluent stream from anammoxidation reactor, comprising a microprocessor; and a FT-IRspectrometer having a sample cell through which may flow a portion ofthe effluent stream, an infrared source to emit infrared radiation andpass the infrared radiation through the effluent stream, an infrareddetector to detect transmitted infrared radiation at selected infraredwavelengths and to generate absorbance data due to absorbance of theinfrared radiation by the components, wherein each of the componentsabsorbs infrared radiation at one or more of the infrared wavelengths,and an output apparatus to provide the absorbance data to themicroprocessor; wherein the microprocessor is programmed to identify andquantify each of the plurality of components based upon the absorbancedata and calibration data, the calibration data being obtained fromrecovery run analyses and FT-IR calibration analyses in the sample cell.

[0007] In one embodiment, the invention is a method for identifying andquantifying components in an effluent stream from an ammoxidationreactor, comprising (A) advancing a portion of the effluent streamthrough a sample cell in a FT-IR spectrometer; (B) scanning the portionin the sample cell with infrared energy at a plurality of infraredwavelengths, wherein each of the components absorbs the infrared energyat one or more of the plurality of selected wavelengths; (C) detectingthe infrared radiation passing through the sample cell and generatingabsorbance data for each of the components; and (D) quantifying each ofthe components by comparing the absorbance data to a calibration curvefor each component in a microprocessor programmed to quantify each ofthe components.

[0008] In one embodiment, the invention is a method for controllingoperation of an ammoxidation reactor based upon real-time quantitativeanalysis of components in an effluent stream from the ammoxidationreactor, comprising (a) preparing a calibration curve for each of thecomponents by analyzing a plurality of effluent streams each containingthe plurality of components by a calibration process comprising: (a-1)advancing at least a portion of each effluent stream through a samplecell in a FT-IR spectrometer; (a-2) scanning the effluent streamadvancing through the sample cell with infrared energy across a range ofinfrared wavelengths and obtaining absorbance data at selectedwavelengths across the range of infrared wavelengths; (a-3) collectingat least one sample corresponding to each effluent stream; (a-4)performing a recovery run analysis on the at least one sample to obtainquantitative data for each of the components in each sample; and (a-5)determining the calibration curve for each of the components bycorrelating the absorbance data and the quantitative data; (b) obtainingreal-time absorbance data for each of the components in an operationaleffluent from the ammoxidation reactor by performing steps (a-1) and(a-2) thereon and calculating in a microprocessor programmed thereforreal-time quantitative data for the operational effluent from thecalibration curve and the real-time absorbance data; and (c) controllingthe ammoxidation reactor to optimize production of at least one of thecomponents based on the real-time quantitative data.

[0009] In one embodiment, the ammoxidation reactor is operated so as toproduce acrylonitrile. In one embodiment, the acrylonitrile is producedfrom a propylene feed. In one embodiment, the acrylonitrile is producedfrom a propane feed. While the following description particularlydescribes the invention as applied to an acrylonitrile reactor, it is tobe understood that this is for illustrative purposes only, and theinvention, applicable broadly to ammoxidation reactors, is not solimited.

[0010] Thus, the present invention provides the real-time informationneeded to allow improved control and immediate, on-going optimization ofthe reaction in an ammoxidation reactor during occurrence of thereaction for which the information is obtained, thus providing the longsought “real-time” analyses and process control.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011]FIG. 1 is a schematic diagram of the optics portion of an FT-IRspectrometer.

[0012]FIG. 2 is a schematic diagram of an acrylonitrile reactor andFT-IR analysis apparatus in accordance with one embodiment of thepresent invention.

[0013]FIG. 3 is a schematic diagram of an apparatus for analyzing theeffluent from an acrylonitrile reactor and providing operative controlof the reactor, in accordance with one embodiment of the presentinvention.

[0014]FIG. 4 is an FT-IR spectrum of an exemplary sample of an effluentfrom an acrylonitrile reactor.

[0015] FIGS. 5A-5J are FT-IR spectra of ten individual components of aneffluent of an acrylonitrile reactor in which propylene is thehydrocarbon feed, each figure showing the spectrum of an individualcomponent.

[0016] FIGS. 6A-6J are graphs of FT-IR mole % value vs. recovery runmole % values for ten individual components of an effluent of anacrylonitrile reactor in which propylene is the hydrocarbon feed, inwhich the FT-IR data has been validated, each graph showing the valuesfor an individual component.

[0017]FIG. 7 is a graph of FT-IR mole % value vs. recovery run mole %values for acrylonitrile from the effluents of two differentacrylonitrile reactors in each of which different grades of propylenewere the hydrocarbon feed, based on recovery run analyses performed bydifferent analysts in different laboratories, and in which the FT-IRmole % was obtained on different FT-IT spectrometers.

[0018]FIG. 8 is a graph of FT-IR mole % value vs. recovery run mole %values for acrylonitrile from an effluent of an acrylonitrile reactor inwhich propylene is the hydrocarbon feed, in which the model is used onall data from FT-IR mole % values and all data from recovery run mole %values.

DETAILED DESCRIPTION

[0019] Fourier Transform Infrared (FT-IR) spectroscopy uses energy inthe form of infrared (IR) radiation, which is between the visible andmicrowave regions of the electromagnetic spectrum. FIG. 1 is a schematicdiagram of the optics portion of a FT-IR spectrometer 1 such as thatused to obtain IR spectra of the combined effluent of an acrylonitrilereactor in accordance with the present invention. The FT-IR spectrometer1 shown in FIG. 1 schematically represents, in one embodiment, a BOMEMMichelson FT-IR spectrometer, which includes a Michelson interferometer.In the FT-IR spectrometer a sample to be analyzed is placed in or passedthrough a FT-IR sample cell 2 which is specially designed to transmitlight at infrared (IR) wavelengths. IR radiation generated by abroad-spectrum IR source 3 passes through the sample cell 2, and travelson to a sensitive IR detector 4. The IR radiation from the source 3 issplit by an interferometer 5 such that the IR radiation follows pathshaving slightly different pathlengths through the sample cell 2 and tothe IR detector 4. The different pathlengths correspond to differentwavelengths of the IR radiation. Some wavelengths of the IR radiationmay be absorbed by the sample, indicating the presence of particularmolecular bonds by the presence of characteristic adsorption bands. Thedetector 4 generates a signal based on changes in the relativeintensities of the IR radiation arriving at the detector 4 by the twodifferent paths, one of which passes through the FT-IR sample cell 2, asit scans through a spectrum of IR radiation. A computer performs aFourier transform to convert the time-modulated intensity changes into aspectrum of absorption vs. wavelength for the sample. The instrumentproduces a graph of absorption against wavelength of radiation called anIR spectrum. Analysis of the characteristic spectral absorption bandsallows identification of the composition of the sample. The signal usedto generate the graph also may be sent to a memory device in amicroprocessor for calculation of numerical results. The sample cell 2in FIG. 1 is shown in phantom since its actual dimensions and positionmay vary widely depending upon its arrangement with respect to thesample source, such as in an industrial production setting, for example,an acrylonitrile reactor.

[0020] In the present invention, the FT-IR spectral absorption bands areanalyzed for a plurality of components in the effluent from anacrylonitrile reactor. However, each of the plurality of components isnot separately analyzed in a pure state to identify its distinctiveabsorption bands. Rather, many spectra are collected on actual effluentsfrom the acrylonitrile reactor, the corresponding recovery run analysesperformed, and by use of sophisticated computer algorithms, qualitativeand quantitative correlations are developed for each of a plurality ofthe components in the effluent stream. The actual effluents from theacrylonitrile reactor contain the combined plurality of components whichwill be found in later operational effluents. The relative quantities ofeach of the plurality of components in the effluents used forcalibration are thus similar to the relative quantities of thecomponents in the operational effluents. As a result any interferencesshould be similar, and be cancelled out.

[0021] As a result of the quantitative correlations, calibration curvesare developed by which each of the components in the effluent from theacrylonitrile reactor may be identified and quantified. Thus, theeffluent from an acrylonitrile reactor is repeatedly analyzed, andparticular absorption bands unique to each of the plurality ofcomponents are identified by the computer program and correlated withthe quantities of each component as provided by conventional recoveryrun analyses. Details of the procedure used to obtain the calibrationcurves are provided in the following.

Ammoxidation Reactors and FT-IR Apparatus

[0022] Any source of oxygen may be employed in the ammoxidation reactionprocess. In one embodiment, air is used. In one embodiment, a mixture ofoxygen and nitrogen is used. For economic reasons it is generallypreferred that air be employed as the source of oxygen. In oneembodiment molecular oxygen is used and gives similar results. Forprocess control reasons, a mixture of oxygen and nitrogen, such as air,is preferred due to the more precise control obtained when controllingthe flow of a larger volume of gas. In one embodiment, the molar ratioof oxygen to the hydrocarbon in the feed to the reaction vessel is inthe range from about 0.5:1 to about 4:1. In one embodiment, the molarratio is from about 1:1 to about 3:1.

[0023] The molar ratio of ammonia to hydrocarbon in the feed to theammoxidation reactor may vary between about 0.05:1 to about 5:1. Thereis no real upper limit for the ammonia/hydrocarbon ratio, but there isgenerally no reason to exceed the 5:1 ratio. At ammonia-hydrocarbonratios appreciably less than the stoichiometric ratio of 1:1, variousamounts of oxygenated derivatives of the hydrocarbon will be formed.Within the ammonia-hydrocarbon range stated, maximum utilization ofammonia is obtained and this is highly desirable. It is generallypossible to recycle any unreacted hydrocarbon and unconverted ammonia.When paraffins are used as feed the process continuously recycles boththe feed paraffin and ammonia.

[0024] In one embodiment, water is included in the feed. In embodimentsusing fixed-bed systems, water may improve the selectivity of thereaction and the yield of nitrile. In one embodiment, the molar ratio ofwater to olefin is in the range from about 0.1:1 and higher. In oneembodiment, the ratio is in the range from about 1:1 to about 6:1. Inone embodiment, the ratio is up to about 10:1.

[0025] In one embodiment, the reaction is carried out at an elevatedtemperature in the range from about 200° C. to about 600° C. In oneembodiment, the reaction is carried out at a temperature in the rangefrom about 400° C. to about 550° C. In one embodiment, the reaction iscarried out at a temperature in the range from about 420° C. to about500° C. In one embodiment, the reaction is carried out at a temperatureof about 420° C. In one embodiment, the reaction is carried out atpressures from about atmospheric to about 2 to 3 atmospheres. Ingeneral, high pressures, i.e. above 15 atmospheres, are not suitablesince higher pressures tend to favor the formation of undesirablebyproducts.

[0026] The apparent contact time is not critical, and contact times inthe range of from 0.1-40 seconds may be employed. The optimal contacttime will, of course, vary depending upon the reactant being used. Inone embodiment, the contact time is from about 1 to about 15 seconds.

[0027] The ammoxidation reaction is generally carried out in the vaporphase. The reaction product passing out of the reactor is normally inthe form of a vapor. This gaseous reaction product is treated to removeNH₃ and then partially condensed either by indirect contact with acooling medium or direct contact with water to form a liquid phasecontaining acrylonitrile, acrolein, acrylic acid, HCN and acetonitrileand a vapor phase containing CO₂, CO, N₂ and O₂. The acrylonitrile isthen separated from the liquid phase by one of a number of differenttechniques such as, for example, distillation or waterextraction/distillation. Additional steps can be employed to separatelyrecover HCN and/or acetonitrile from the gross reaction product. Inaddition, any excess ammonia may be recovered and recycled.

[0028]FIG. 2 is a schematic diagram of an acrylonitrile reactor andFT-IR analysis apparatus in accordance with the present invention. It isnoted that many of the process elements shown in FIG. 2 include bothdashed and solid lines. Such combined dashed and solid lines indicatethat these process elements are heated, and should be maintained at anelevated temperature during normal operations. Specific temperaturesvary somewhat depending on the particular process element, and areprovided in the following.

[0029] The apparatus shown in FIG. 2 includes an acrylonitrile reactor10. The acrylonitrile reactor 10 shown in FIG. 2 is a pilot-scale unit.The ammoxidation reaction takes place in the acrylonitrile reactor 10.In one embodiment, the reactor 10 is a fluid bed reactor containingsolid, loose particles of catalyst. In one embodiment, the reactor 10 isa fixed bed reactor, in which the catalyst is attached to a solid, fixedsupport. The reactor 10 may be disposed in a mass such as sand bath 12,in order to provide a heat source for the high temperatures employed inthe ammoxidation reaction. While the ammoxidation reaction is anexothermic reaction, in a pilot scale reactor such as the reactor 10shown in FIG. 2, additional heat must be applied to maintain the reactor10 at the desired temperature level for the reaction. The sand bath 12is heated from an external source and is the source of heat in the pilotscale reactor 10 in addition to the heat generated by the ammoxidationreaction. The following description, while generally applicable toeither a fluid or fixed bed ammoxidation reactor, is particularlydirected to the fluid bed reactor. Those skilled in the art willrecognize and understand the differences, and will recognize that thepresent invention is not limited to a particular type of ammoxidationreactor.

[0030] As shown in FIG. 2, the reactor 10 may include a baffle plate 14in the lower portion of the reactor 10. The three feed materials, i.e.,the hydrocarbon feed, ammonia and oxygen (in one embodiment in the formof air), are injected into the lower portion of the reactor 10. The airis injected via an air line 16 below the baffle plate 14. Thehydrocarbon feed and ammonia are injected just above the baffle plate 14via a heated combined feed line 18.

[0031] As shown in FIG. 2, the reactor 10 may further include a seriesof mixing plates 20, which ensure thorough and intimate contact betweenthe gaseous reactants and the solid particles of catalyst in thereactor. The mixing plates 20 shown in FIG. 2 are schematic, and it isto be understood that various embodiments of mixing elements may beused. In the upper portion of the reactor 10 is a filter or strainer 22.The strainer 22 allows the gaseous products and any un-reacted gaseousreactants to pass, but prevents passage of solid materials such as thecatalyst particles. In one embodiment, the strainer 22 may be acyclone-type separator. In general, the strainer 22 is an apparatus forseparating solids and gases. The interior of the reactor 10, via thestrainer 22, is in fluid connection with an effluent line 24. Theeffluent line 24 carries the effluent from the acrylonitrile reactor toa scrubber 26, and thence to product recovery and, optionally, reactantrecycling apparatus (not shown). The effluent line 24 is heated tomaintain all reactor products in the gaseous state, as set forth aboveand shown in FIG. 2. All lines carrying effluent to the scrubber 26should be heated, and such heating should be to a relatively constanttemperature.

[0032] As shown in FIG. 2, the effluent line 24 may include a 3-way orT-connection 28 at which a side stream effluent line 30 may be split offfrom the primary effluent line 24. The side stream line 30 may besubstantially smaller than the primary effluent line 24. The side streamline 30 is heated, as set forth above and shown in FIG. 2. The sidestream line 30 also includes a shut-off valve 32 and a purge valve 34. Asample cell 36 of an FT-IR spectrometer is disposed in the side streamline 30. The sample cell 36 in FIGS. 2 and 3 corresponds to the samplecell 2 in FIG. 1. While it is possible to have the sample cell 36disposed in the primary effluent line 24, in a commercial process thiswould not be preferred, due to the large volume of gaseous productsexiting the ammoxidation reactor 10 (FIG. 2) or a commercial scaleammoxidation reactor 100 (FIG. 3), and the concomitant large size samplecell which would be required.

[0033] As shown in FIG. 2, first and second nitrogen purge lines 38, 40are provided. The first nitrogen purge line 38 is provided to purge theentire side stream line 30, and is used to remove traces of the effluentprior to, e.g., service of the sample cell 36 or shutdown of the reactorsystem. When the 3-way valve 28 is set so that the side stream line 30is closed off from the effluent line 24, the shut-off valve 32 is closedand the purge valve 34 is opened, nitrogen from the first nitrogen line38 may be used to purge the entirety of the side stream line 30,including the sample cell 36. The second nitrogen purge line 40 isprovided to purge only the sample cell 36, such as for calibration andbackground determinations. In order to purge the sample cell 36, itwould be necessary to close a cell shut-off valve 42 to prevent entry ofeffluent from the reactor, to close the shut-off valve 32 and to open atleast one purge valve, such as the purge valve 34, while passingnitrogen into and through the sample cell 36.

[0034] As shown in FIG. 2, the effluent line 24 includes a sample valve44. The sample valve 44 may be used for removing samples for chemicalanalysis, such as for recovery run analysis. Alternatively, or inaddition, recovery run samples may be removed from a second sample valve46. In the recovery run analyses used for calibration of the FT-IRspectrometer in the present invention, the samples were collected fromthe second sample valve 46, downstream from the sample cell 36.

[0035]FIG. 3 is a schematic diagram of an apparatus for analyzing theeffluent from an acrylonitrile reactor and providing operative controlof the reactor, in accordance with one embodiment of the presentinvention. Where the elements of FIG. 3 are the same as in FIG. 2, thesame reference numbers are used, and the description thereof is omitted.For simplicity, some elements are not shown in FIG. 3.

[0036] The reactor system shown in FIG. 3 includes the commercial-scalereactor 100. The reactor 100 differs is some aspects from the reactor 10shown in FIG. 2 and described in relation thereto. The reactor 100 doesnot include a sand bath as a heat source. In a commercial scale reactor,such as the reactor 100, a greater quantity of heat is generated in theexothermic ammoxidation reaction than necessary to sustain the reaction.In the reactor 100, rather than adding heat to sustain the reaction,heat must be removed to control the reaction. Thus, as will be describedmore fully below, controlling the temperature of the reactor 100 isgenerally performed by adjusting the amount of heat removed from thereactor, rather than adjusting the amount of heat added, as is the casefor a smaller reactor, such as the reactor 10 shown in FIG. 2.

[0037] The reactor 100 includes a means for distributing the gaseousfeed materials to the reactor. In one embodiment, the reactor 100includes a first sparger 102 and a second sparger 104. The first sparger102 feeds and distributes the air or other oxygen-containing feedmaterial to the reactor 100. As shown in the embodiment of FIG. 3, thefirst sparger 102 may be disposed below or near the level of the baffleplate 14. The second sparger 104 feeds and distributes the ammonia andhydrocarbon feed materials to the reactor 100. As shown in theembodiment of FIG. 3, the second sparger 104 may be disposed above thelevel of the baffle plate and above the level of the first sparger 102.In other embodiments, other known distribution devices may be employed,such as one or more jet mixers, injectors, baffled-flow mixers, multiplenozzle-type inlets or multiple spargers.

[0038] As shown in FIG. 3, the apparatus includes an FT-IR spectrometer48, of which the sample cell 36 is a part. The FT-IR spectrometer 48includes optics, an IR source, and an IR detector, such as those shownin FIG. 1, and circuitry to measure the strength of the IR radiationoriginating from the IR source and reaching the IR detector as well asthe sample cell 36. The FT-IR spectrometer 48 further includes circuitryto generate absorbance data, and an output device capable of outputtingthe absorbance data together with associated information such as thewavelength of IR radiation. Details of the structure and operation of anFT-IR spectrometer are known to those of skill in the art and will notbe further set forth herein, except as may be needed to disclose thepresent invention.

[0039] As shown in FIG. 3, the FT-IR spectrometer 48 is connected to amicroprocessor 50 via a FT-IR output line 52. The FT-IR spectrometer 48outputs the absorbance data together with the associated informationsuch as the wavelength of IR radiation, via the output line 52 to themicroprocessor 50. The microprocessor 50 is linked via an input line 54to an input device 56 by which calibration information and data,recovery run data and other information may be input to themicroprocessor 50. The input device 56 may comprise a keyboard formanually inputting data, and may be linked to an additional, possiblyremote, microprocessor which generates or provides calibration data. Themicroprocessor 50 may also be linked to other sources, such as theoutput from a data collection memory device. The microprocessor 50 isprogrammed to quantify the components in the effluent from theacrylonitrile reactor 100, based on the absorbance data provided by theFT-IR spectrometer 48, and on the calibration data provided by the inputdevice 56.

[0040] As shown in FIG. 3, the microprocessor 50 is attached via anoutput line 58 to a reactor controller 60. The reactor controller 60controls the operation of the reactor 100, communicating with thereactor 100 by a reactor control line 62. The reactor controller 60operates a set of mass flow control valves for the reactants fed to thereactor. In one embodiment, the various control valves associated withthe acrylonitrile reactor are pneumatically operated, and are actuatedwhen the microprocessor transmits an electrical signal to a transducerwhich converts the electrical into a pneumatic signal. The pneumaticsignal in turns opens or closes or adjusts the respective control valvesin known fashion.

[0041] The mass flow control valves for the reactants include an aircontrol valve 64, a hydrocarbon feed control valve 66 and an ammoniacontrol valve 68. As described with respect to the air line 16 in FIG.2, and as shown in FIG. 3, the air line 16 carries the air, the flow ofwhich is controlled by the air control valve 64, to the reactor 100.Similarly, the combined feed line 18 carries the combined hydrocarbonand ammonia feed to the reactor 100, the flow of each controlledrespectively by the hydrocarbon control valve 66 and the ammonia controlvalve 68.

[0042] Based on the output from the microprocessor 50, received via theinput line 58, the reactor controller 60 also controls reactorconditions, such as internal reactor temperature and pressure,temperatures of the feed lines 16 and 18, and temperature of theeffluent line 24, via the reactor input line 62. The reactor controller60 controls the flow of cooling water to the reactor 100, by which thetemperature of the reactor 100 is controlled. The cooling system is notshown in the drawings. The reactor conditions, the flows of thereactants and other parameters, such as the reactor temperature and therate of addition or removal of heat, can be precisely controlled by thereactor controller 60 based on the output from the microprocessor 50 andthe desired distribution of products, which may be set by, e.g., a humanoperator. As will be understood by those of skill in the art, thereactor controller 60 may include, e.g., a further microprocessor andinput device by which a human operator can select variables to adjustand optimize production of particular products in the reactor 100.

[0043] In one embodiment, the sample cell 36 in the FT-IR spectrometer48 used with the acrylonitrile reactor is constructed of stainless steelwith ZnSe windows through which the IR passes, with the effluent fromthe acrylonitrile reactor passing through the interior of the samplecell 36 between the windows, thereby resulting in the IR beam passingthrough the effluent. In one embodiment, the pathlength in the samplecell 36 is 10 cm. The temperature in the sample cell 36 is maintained atan elevated level, as described above with respect to the effluent linesgenerally, in order to maintain the effluent in a uniform, gaseousstate. In one embodiment, the temperature in the effluent line and thesample cell 36 is maintained at about 200° C. In one embodiment, thetemperature of the line and the sample cell 36 is maintained at about150°C. It is important that the temperature in the sample cell 36 remainconstant, and that the temperature in the sample cell 36 be the sameduring calibrations and during production runs using the calibrations asset forth herein.

[0044] Sample FT-IR spectra are shown in FIGS. 4 and 5A-5J. In eachspectrum shown in FIGS. 4 and 5A-5J, the resolution is 1 cm⁻¹. FIG. 4 isan FT-IR spectrum of an exemplary combined effluent from anacrylonitrile reactor, obtained in an apparatus such as that shownschematically in FIG. 3, at 200° C. and scaled such that the largestpeaks are full scale on the Y-axis. The FT-IR spectrum of FIG. 4includes wavenumbers in the range from 500 cm⁻¹ to 4000 cm⁻¹. In thepresent specification, as is common in the art, in the IR region of theelectromagnetic spectrum, wavelength is expressed in terms of wavenumberper centimeter, i.e., the number of waves per centimeter, or simplyreciprocal centimeters, cm⁻¹.

[0045] Each of FIGS. 5A-5J is an FT-IR spectrum of one of tencomponents, of the plurality of components, which are routinely found inmeasurable and significant quantities in the effluent from anacrylonitrile reactor, collected at room temperature (RT), scaled suchthat the largest peaks are full scale on the Y-axis, and “smoothed” toreduce the resolution to match that of the spectra of the combinedeffluent obtained at elevated temperature, such as that shown in FIG. 4.FIG. 5A is an FT-IR spectrum of acrylonitrile (AN) in the region 1200cm⁻¹ to 801 cm⁻¹. FIG. 5B is an FT-IR spectrum of acetonitrile in theregion 1200 cm⁻¹ to 801 cm⁻¹. FIG. 5C is an FT-IR spectrum of propylene(Pro) in the region 1200 cm⁻ ¹ to 801 cm⁻¹. FIG. 5D is an FT-IR spectrumof ammonia (NH₃) in the region 1200 cm⁻¹ to 801 cm⁻¹. FIG. 5E is anFT-IR spectrum of hydrogen cyanide (HCN) in the region 3350 cm⁻¹ to 3250cm⁻¹. FIG. 5F is an FT-IR spectrum of carbon monoxide (CO) in the region2230 cm¹ to 2050 cm⁻¹. FIG. 5G is an FT-IR spectrum of carbon dioxide(CO₂) in the region 2400 cm to 2300 cm⁻¹. FIG. 5H is an FT-IR spectrumof water (H₂O) in the region 2133 cm⁻¹ to 1241 cm⁻¹. FIG. 5I is an FT-IRspectrum of acrolein (Aln) in the region 3500 cm⁻¹ to 801 cm⁻ ¹. FIG. 5Jis an FT-IR spectrum of acrylic acid (AA) in the region 4000 cm¹ to 801cm⁻¹.

[0046] The spectra of each of the ten components were collected at RT,while the spectra of the combined effluent of the acrylonitrile reactorwere collected at approximately 200° C. The separate spectra of theseten components are shown for exemplary purposes. The peaks of each ofthe ten components present in the combined effluent from theacrylonitrile reactor appear in the spectrum thereof, although the peaksmay overlap with those due to other components and may be slightlyshifted in wavenumber due to the temperature difference. For this andother reasons, the calibration curves for the present invention shouldbe obtained using the combined effluent, and recovery run analyses onthe combined effluent, rather than by attempting to obtain calibrationcurves based on spectra for each individual component.

[0047] The FT-IR spectra shown in FIGS. 4 and 5A-6J were collected bymeans of a BOMEM Michelson FT-IR spectrometer, as shown schematically inFIG. 1 and described above. The effluent, or a portion thereof, from theacrylonitrile reactor is plumbed to the FT-IR sample cell 36 mounted inthe BOMEM Michelson FT-IR spectrometer. The output from the spectrometer48 is sent to the microprocessor 50 on which is loaded dedicatedsoftware, such as the CAAP software from BOMEM, described below. Forpreparation of calibration curves, at the same time the FT-IR data isbeing collected, a sample of the effluent is collected for recovery runanalysis. In general, a plurality of FT-IR spectra are collected foreach recovery run sample collected, when data is being collected forpreparing calibration curves.

Identification and Quantitative Calibration Procedures

[0048] The dedicated software used in developing the present inventionis the BOMEM Continuous Automated Analysis Program (CMP). The BOMEM CAAPsoftware is designed for use with the BOMEM FT-IR spectrometer usedherein. The BOMEM CMP software is used in obtaining calibration data andcalibration curves, and in adaptation of the BOMEM FT-IR spectrometersystem to the uses described herein. A program was written using CMPsoftware, which records and saves the spectra of the effluent passingthrough the FT-IR sample cell. The program makes use of the CMPsoftware, but works within it, much like a macro in a spreadsheet orword processing program.

[0049] In developing calibration curves for the present invention, atotal of 420 FT-IR spectra were recorded and grouped into 42 groups of10 spectra each. Each of the ten spectra in a group corresponds to onerecovery run analysis. Each set of ten spectra was recorded over aperiod of 24 minutes, during which time the recovery run sample wascontinuously collected. Of the 42 spectra, 38 spectra actuallycorresponded to separate recovery run samples, duplicate groups ofspectra having been recorded for some effluent samples, for which onlysingle recovery run samples were collected.

[0050] Each FT-IR spectrum in each set of ten such spectra is examinedfor the presence of anomalous noise, unexpected signals, evidence ofanalyzer problems, and consistency between the ten spectra. One spectrumis selected as representative. In the absence of some indication ofdefect in the spectra, the fifth spectrum in the series of ten isgenerally selected to be used for development of the calibrationequations or curves. Thus, the calibration set described as an exampleherein consisted of 38 spectra and 38 associated sets of recovery runanalytical results.

[0051] A partial least squares (PLS) analysis may be used to examineboth absorption band variables and concentration data variables. PLSextracts components called factors directly relevant to both sets ofvariables. These components are extracted in decreasing order ofrelevance. PLS actually uses the concentration information during thedecomposition process to assist in identifying relevant absorptionbands. Spectra containing higher constituent values are weighted moreheavily than those with low values. This process generates one set ofvectors and corresponding scores for the spectral data, and one set ofvectors and corresponding scores for the component concentrations. Thetwo sets of scores are regressed and a correlation vector obtained,which is referred to herein as the calibration curve for the componentsin the effluent and the associated FT-IR spectral data.

[0052] In order to cross-validate the method and resulting calibrationcurves, approximately 30% of the spectra and associated recovery runresults are left out of the calibration procedure, and are lateranalyzed. Where the data or the calibration curves herein are stated tohave been validated, the procedure includes withholding approximately30% of the FT-IR data and associated recovery run data from thecalculation, determining the calibration curves, and then the withhelddata are integrated one by one and the calibrations redetermined. Theprocess of redetermining calibration curves while withholding other datais repeated while the other data is withheld, and so on. This procedureminimizes the possibility of developing models based on correlationsbetween mole % data and random variations in the FT-IR spectra which arenot associated with changes in concentration of the particular speciesunder analysis for development of a calibration curve. In addition tothe foregoing validation procedure, the standard error of calibrationand cross-validation may be calculated.

[0053] During development of the present invention, occasional shifts inthe baseline of the FT-IR spectra were observed. In order to cancel theeffects of such baseline shifts, in every spectrum, immediately aftermeasurement every spectrum is baseline corrected by subtracting theabsorbance value at 2523 cm⁻¹ from every absorbance data point in thespectrum. This correction was programmed and has been used for everymeasurement in the examples set forth herein. This correction should bemade in spectra obtained according to the present invention. Thiscorrection eliminates any effect of baseline shift from the quantitativevalues calculated from the spectra, in both the calibration and theoperational samples.

[0054] The results of the validation procedures and calculations ofstandard error and cross-validation show that the calibration curveclosely tracks the actual concentration of the plurality of componentsin the effluent from the acrylonitrile reactor as provided by standardlaboratory recovery run analyses. The major difference between thesetechniques is that the FT-IR data collection and calculation ofconcentrations of the components in the effluent from the acrylonitrilereactor provides immediate, real-time information on the concentrationsof the components, whereas the recovery run analysis requires many hoursto provide the same quantitative results. Because of the temporallimitations of recovery run analysis, it cannot provide the real-timeinformation needed to control the operation of an acrylonitrile reactorin real-time. Thus, the techniques of FT-IR and computer analysis fromcalibration curves provides timely information which can be used toimmediately control and optimize the operation of the acrylonitrilereactor in real-time.

[0055] The following Table shows, for each of ten individual componentswhich may be present in the effluent from an acrylonitrile reactor, thespectral regions, in cm⁻¹, and the standard error of validation (SEV),obtained from an exemplary calibration procedure. TABLE 1 COMPONENTSPECTRAL REGION, cm⁻¹ SEV Acrylonitrile 780-900 0.15 992-1200 1850-2000Propylene 800-920 0.03 Ammonia 844-850 0.18 Carbon Dioxide 2241-22990.11 Carbon Monoxide 2156-2200 0.06 Hydrogen Cyanide 3252-3285 0.04Water 1960-1975 0.25 Acetonitrile  800-1500 0.02 Acrolein 1658-17500.015 Acrylic Acid 1140-1180 0.014

[0056] The SEV is calculated from the following equation:${SEV} = \sqrt{\frac{\sum\limits_{t = 1}^{m}\left( {{Yk}_{i} - {Yp}_{i}} \right)^{2}}{n}}$

[0057] Where Yk is the recovery run concentration, Yp is theconcentration using the FTIR model, and n is the number of samples inthe set of FTIR spectra and recovery run samples. As described herein,the value of Yp for a particular sample was calculated by using a modeldeveloped without that sample in the set, a process known ascross-validation, and the SEV is sometimes referred to as standard errorof cross-validation.

[0058] FIGS. 6A-6J are graphs of FT-IR mole % value vs. mole % valuesobtained from recovery run analyses, for ten components of an effluentof an acrylonitrile reactor in which propylene is the hydrocarbon feed.For each graph, the validated FT-IR mole % values are plotted againstmeasured mole % values obtained from recovery run analyses. FIGS. 6A-6Jare representative of the calibration curves which are used by themicroprocessor of the present invention to calculate the content of eachof the components in the effluent from an acrylonitrile reactor. Thegraph shown in FIG. 6A is a calibration curve for acrylonitrile and isshown with the equation for the line and the value for the correlationcoefficient, R², obtained the data upon which the graph is based. Thegraph shown in FIG. 6B is a calibration curve for propylene and is shownwith the equation for the line and the value for the correlationcoefficient, R², obtained the data upon which the graph is based. Thegraph shown in FIG. 6C is a calibration curve for ammonia (NH₃) and isshown with the equation for the line and the value for the correlationcoefficient, R², obtained the data upon which the graph is based. Thegraph shown in FIG. 6D is a calibration curve for carbon dioxide (CO₂)and is shown with the equation for the line and the value for thecorrelation coefficient, R², obtained the data upon which the graph isbased. The graph shown in FIG. 6E is a calibration curve for carbonmonoxide (CO) and is shown with the equation for the line and the valuefor the correlation coefficient, R², obtained the data upon which thegraph is based. The graph shown in FIG. 6F is a calibration curve forhydrogen cyanide (HCN) and is shown with the equation for the line andthe value for the correlation coefficient, R², obtained the data uponwhich the graph is based. The graph shown in FIG. 6G is a calibrationcurve for acetonitrile and is shown with the equation for the line andthe value for the correlation coefficient, R², obtained the data uponwhich the graph is based. The graph shown in FIG. 6H is a calibrationcurve for acrolein and is shown with the equation for the line and thevalue for the correlation coefficient, R², obtained the data upon whichthe graph is based. The graph shown in FIG. 6I is a calibration curvefor acrylic acid and is shown with the equation for the line and thevalue for the correlation coefficient, R², obtained the data upon whichthe graph is based. The graph shown in FIG. 6J is a calibration curvefor water (H₂O) and is shown with the equation for the line and thevalue for the correlation coefficient, R², obtained the data upon whichthe graph is based.

[0059]FIG. 7 is a graph of FT-IR mole % value vs. recovery run mole %values for acrylonitrile from the effluents of two differentacrylonitrile reactors in each of which different grades of propylenewere the hydrocarbon feed. The graph in FIG. 7 is based on recovery runanalyses performed separately on the effluents of the two acrylonitrilereactors, by different analysts in different laboratories, and by usingseparate FT-IR instruments on the effluents of the two acrylonitrilereactors. The FT-IR data in each case has been validated by theprocedure set forth above. In addition to the graph, the equation forthe line and the value for the correlation coefficient, R², obtained forthe data upon which the graph is based are shown. The data were recordedover a long period of time in order to test both the calibrationprocedure and the long-term stability and reproducibility of the method.The feed gases were of widely varying quality during the time period inwhich this data was collected. This experiment was conducted to assurethat the calibration model developed would include all possiblevariability. This graph shows the robustness of the method of thepresent invention.

[0060]FIG. 8 is a graph of FT-IR mole % value vs. recovery run mole %values for acrylonitrile from an effluent of an acrylonitrile reactor inwhich propylene is the hydrocarbon feed, in which the model is used onall data from FT-IR mole % values and all data from recovery run mole %values, and is shown with the equation for the line and the value forthe correlation coefficient, R², obtained the data upon which the graphis based. In this graph, all available FT-IR data was used to determinethe FT-IR mole % data from the available acrylonitrile calibrationcurves, and was plotted against the recovery run mole % data for thecorresponding samples collected contemporaneously with the respectiveFT-IR data. This graph shows, for acrylonitrile, that excellentcorrelation results can be expected between FT-IR mole % data obtainedby the method of the present invention, based on comparison of the FT-IRmole % data with the recovery run mole % data.

[0061] The pressure in the FT-IR cell may affect the calibration and theresulting analyses. The FT-IR cell has a constant volume and isgenerally held at a constant temperature. An increase in internal thepressure in the FT-IR cell thus correlates to a greater number ofmolecules of the components of the effluent from the acrylonitrilereactor being in the FT-IR cell. Changes in pressure result inbroadening of peaks in the spectra recorded by the FT-IR spectrometer.The peaks may be further distorted due to differential broadening ofindividual peaks, since the effects of pressure are likely different ondifferent bonds in different molecules, the vibration of which givesrise to the IR absorptions. For these reasons, it is important tooperate the FT-IR sample cell at a constant pressure, and, moreimportantly, the pressure in the FT-IR sample cell during an operatingperiod should be the same pressure used to develop the calibration curvedata used for that equipment.

Detailed Statistical Calibration Procedures

[0062] Partial Least Square (PLS) is a technique for quantitativeanalysis which has been applied to spectroscopic and chromatographicdata. The technique ultimately is applied to Beers Law, shown in thefollowing equation:

A₁=C₁K₁

[0063] where A₁ is the absorbance at a given wavelength or wavenumber,C₁ is the concentration of the component giving rise to the absorbance,and K₁ is the absorptivity constant for the component. Using Beer's law,one can calculate the absorptivity of a component with a knownconcentration, then by simply measuring the absorbance and using thecalculated absorptivity constant, calculate the concentration of anunknown sample containing the component of interest. PLS assumes somelinear relationship between the measurement and the concentration of aparticular component, although the technique allows use of manydifferent wavelengths to be used as factors in applying Beer's Law. Fora mixture containing two components, Beers Law can be written for eachcomponent:

A₁=C₁K₁+E₁

A₂=C₂K₂+E₂

[0064] where E₁ and E₂ are the residual errors normally due to theinstrument and sample handling. Provided there is no relationshipbetween the two absorbances (A₁ and A₂), then one can solve eachequation independently. Since Beer's Law is additive, these equationscan be solved simultaneously and hence handle the case where there issome interference within a single spectrum between the two absorbances:

A₁=C₁K₁₁+C₂K₂₁+E₁

A₂=C₁K₁₂+C₂K₂₂+E₂

[0065] When more than one component is involved, matrix methods can beused. In matrix terms the above equations can be written as:

A_((n,p))=C_((n,m))K_((m,p))+E_((n,p))

[0066] where n is the number of calibration samples, p is the number ofwavelengths, and m is the number of components. In matrix notation, theabove equation can be written:

A=CK+E

[0067] Using computers, the equations for the matrix of absorptivitycoefficient and the best least squares line fit to the data can beobtained. Once these equations are solved for the K matrix, the K matrixcan be used to predict concentration of unknowns. This method ofquantitative analysis is known as the K matrix or the Classical LeastSquares (CLS) method.

[0068] If the concentration of one or more components are unknown, thensolving for the K absorptivity coefficients will have some significanterror. One way to resolve this is to rearrange Beer's Law and writeconcentration as a function of absorbance:

C=AP+E

[0069] For a mixture containing two components-the equation can bewritten as:

C₁=A₁P₁₁+A₂P₁₂+E₁

C₂=A₂P₂₁+A₂P₂₂+E₂

[0070] Using the above equations, the absorptivity constant (P) can becalculated even when the concentration of the second component isunknown. Writing concentration as a function of absorbance and solvingfor the P matrix is known as the P matrix or the Inverse Least Squares(ILS) method.

[0071] The PLS algorithm is derived from the CLS and ILS methods, andhas similarity to an algorithm known as PCR. The PLS and PCR algorithmsand their general goals are similar to the CLS and ILS methods. In theexample of a mixture with two components, there are ideally twoindependent variables in the system. That means that the spectra of themixture can be reconstructed by adding together the spectra of purecomponent A and pure component B. In reality, particularly in the caseof IR, this is not so simple. Usually there are some instrument orsample variations or some interaction between pure components A and Bwhich produces some intermediate component or simple changes such as theshape of the bands or the baseline of the spectrum. But even in a systemas complex as an IR spectrum, there are a finite number of independentlyvarying spectra that, when added together, will reconstruct the spectrumof the mixture. In PLS and PCR terms, Beer's Law can be written:

A=TB+E

[0072] where B is the number of the independently varying spectra knownas the loading vectors or loadings factor and T is the amount of eachspectrum that should be added to reconstruct the spectrum of theoriginal mixture and is known as the score. Generally, the amount ofeach spectrum depends on the concentration of the components. Thisprocess reduces the complexity and one can use a small number ofloadings to reconstruct or model the spectra of unknowns. Since the sameloadings are always used to model the unknown, the only differences inthe spectra of different component concentrations is the amount of eachloadings or the scores. Since the scores are unique to the concentrationof each individual component and the training spectrum, then theabsorbance term can be replaced by scores in both CLS and ILS equations.

[0073] In order to create a simplified representation of data, i.e.,scores and loadings, spectral decomposition has been used. This processis also known as Principal Component Analysis (PCA) and it proceeds asfollow:

[0074] 1. Calculate the average of all the training (calibration)spectra.

[0075] 2. Compare each spectrum in the training set to the averagespectrum and calculate the variances between them. This is the firstloading vector.

[0076] 3. Calculate the amount of loading vector in each spectrum in thetraining set. This value is the score.

[0077] 4. Calculate the contribution of the loading (loading vectortimes the score) for each spectrum in the training set and subtract thisfrom each training spectrum.

[0078] 5. Use the resultant training data and substitute it for theoriginal data. Then step 1 to 5 is repeated for each individual trainingsamples.

[0079] The calculated scores are used to perform ILS to calculate theconcentration for each training sample. Performing PCA or spectraldecomposition to obtain a reduced representation of the data andperforming regression to obtain the calibration matrix is known as thePCR algorithm.

[0080] PLS is related to PCR and both use spectral decomposition.However, the main difference between the two techniques is how thedecomposition step is preformed. In PCR, the spectra are decomposedbased on the maximum variances between the data. In PLS, the spectraldecomposition is weighted according to the concentrations. This meansthat the absorption bands with higher values are weighted more heavilythan those with the lower values. Thus, the loading vectors calculatedusing PLS are quite different from the PCR loading vectors.

[0081] In PLS and PCR, the data compression step is achieved byretaining only the part of the information which is significant anddiscarding noise (or unrelated information). Generally, the significantinformation part is assumed to lie within the first few factors and thenoise portion is restricted to the remainder of the factors. The userhas to decide what number of factors are essential for the calibration.One way of selecting the optimum number of factors is by using the PRESScriterion as outlined in the following.

[0082] Determining the number of factors to use in PLS or PCR model isthe most important step in building a model. A balance must be obtainedby using sufficient factors to describe significant variances in thedata set but not using too many factors and overfitting the model.

[0083] One way to determine the optimum number of factors is byevaluating the PRESS (Predicted Residual Error Sum of Squares) values.PRESS is calculated as follows:

[0084] 1. Leave one of the samples in the training set out use theremainder to perform the decomposition step, and obtain the calibrationmatrix.

[0085] 2. Predict the concentration of the sample that has been left outby using the calibration matrix.

[0086] 3. Calculate the difference between the predicted concentrationand the actual. This is a single PRESS value.

[0087] 4. Now include back the sample that was left out previously andleave another sample out and predict the concentration using one factor.Calculate the square sum of residual and add this to the previous PRESS.

[0088] 5. Repeat step 1 to 4 for all the set factors in the calibration.

[0089] Another way to determine the optimum number of factors is tocalculate the standard error of prediction (SEP). SEP is defined as theSEP = [(∑d² − N^(*)D²)/N − 1]^(1/2)

[0090] Where d is the difference between the predicted and actualvalues; D is the mean of the difference between the predicted and actualvalues, and N is the number of samples in the training set The SEVprovides an overall estimate of the quality of the correlation, andprovides a guide for use in selection of the number of factors (i.e., IRabsorption bands) to use in the PLS analysis.

[0091] In the ideal case, both PRESS and SEP values should be at theirmaximum within the first few factors, then they should decrease, reach aminimum, and then increase again. Theoretically, the point where PRESSand SEP reach their minimum is the optimum number of factors for themodel.

[0092] In PLS there is generally three types of outliers, as follows:

[0093] (a) spectra which differ from those of other samples in thetraining set, such as spectra with poorly resolved or anomalousabsorption bands;

[0094] (b) spectra of samples with erroneous concentration values, suchas from contaminated samples;

[0095] (c) spectra of samples with concentration values quite differentfrom those of other samples.

[0096] Outliers of type (a) and (b) are due to instrumental errors andpoor sample handling techniques. Once these types are identified asoutliers, they should be discarded from the calibration model. Outliersof type (c) are also known as “unique” samples. These types of samplesare unique because of their concentration values, either too high or toolow in comparison to the rest of the samples in the training set. Theabsence of more samples with concentrations at the same level makesthese samples stand out from the rest and they are labeled “unique”.Whether these samples are included depends on a number of factors. Ofparticular concern is whether such values are expected during theoperation of the system for which the calibration is prepared. It isimportant to recognize and differentiate such unique samples from thoseof type (a) and (b). A problem may arise if a unique sample is includedin the calibration set which may more properly be a type (a) or (b)outlier. In such a case, a false calibration range might be obtained, ifsuch a sample is not within the range of the samples from the systemunder study. For example, consider a calibration set with 10 sampleshaving concentrations ranging from 2 to 15 units. If a sample having aconcentration value of 30 is included in the final calibration set, andthe correct calibration range should have been 2 to 15 units, thecalibration range actually obtained will be 2 to 30 units. In such case,results falling in the 15 to 30 range may not be accurate.

[0097] The calibration data handling procedure, including preparation ofthe calibration curve may be performed in a microprocessor. In oneembodiment, the microprocessor is programmed to calculate calibrationdata for and to quantify each of the components in the effluent from anacrylonitrile reactor. In one embodiment, the microprocessor isprogrammed to output the quantitative results for each of the pluralityof components. In one embodiment, the quantitative data is output to areactor controller communicating with the acrylonitrile reactor. In oneembodiment, the reactor controller is adapted to adjust and controloperation of the acrylonitrile reactor based on the quantitative data.In one embodiment, the reactor controller is controlled by amicroprocessor programmed to control and optimize the acrylonitrilereactor based on the quantitative data derived from the FT-IR. In oneembodiment, the microprocessor which controls the reactor controller isthe same microprocessor which is programmed to calibrate and quantifyeach of the components in the effluent from the acrylonitrile reactor.In one embodiment, the microprocessor which controls the reactorcontroller is a different microprocessor, and the microprocessor isprogrammed to control the reactor controller, and to thereby control andoptimize the acrylonitrile reactor.

[0098] The following Table 2 includes exemplary results which may beobtained by the system of the present invention. The data shown in Table2 represent three test runs, in which the apparatus and methods of thepresent invention were employed to control and optimize the operation ofan acrylonitrile reactor based upon real-time quantitative analysis ofthe components in an effluent stream from the reactor. In performing theexperimental methods described herein, the apparatus as described hereinwas used. The apparatus included a microprocessor and a FT-IRspectrometer, where the microprocessor was programmed to identify andquantify each of the plurality of components based upon absorbance dataobtained by the FT-IR spectrometer, and upon calibration data obtainedfrom recovery run analyses in the FT-IR sample cell.

[0099] For each of the experiments, a calibration curve was used foreach of the plurality of components which had been previously preparedas described herein. For each of the experiments, initial real-timeFT-IR absorbance data was obtained for the operational effluent from theacrylonitrile reactor before control by the microprocessor and reactorcontroller was initiated. The initial FT-IR absorbance data was used tocalculate the conversion and yield, as shown in the “BEFORE” columns inTable 2. The initial FT-IR absorbance data was input to themicroprocessor which calculated real-time quantitative data for theoperational effluent from the FT-IR absorbance data and the calibrationcurves for the components. Control of the reactor was then transferredto the microprocessor and reactor controller which was then allowed tocontrol the operation of the acrylonitrile reactor to optimizeproduction of the acrylonitrile component based on the quantitativedata. The resulting absorbance data for the optimized operationaleffluent is shown in the “AFTER” columns in Table 2. As a confirmationof the quantitative data obtained by the method of the present inventionshown in the AFTER column, the data shown in the “RECOVERY RUN” columnsin Table 2 was obtained by standard chemical, recovery run, analysis. Asis shown, the method of the present invention provides excellent resultsby controlling, and thereby optimizing, operation of the acrylonitrilereactor. As is shown by the recovery run results, the real-time resultsfor conversion and yield obtained by the FT-IR and microprocessor agreesquite well with the results obtained by the more time-consuming chemicalrecovery run analyses. TABLE 2 Ex- am BEFORE AFTER RECOVERY RUN pleConversion Yield Conversion Yield Conversion Yield 1 73.123 56.443 98.573.51 97.57 75.31 2 78.864 62.886 98.5 77.89 98.31 78.40 3 65.204 49.60198.5 73.40 98.66 75.05

[0100] While the invention has been described in conjunction withspecific embodiments herein, it is evident that many alternatives,modifications and variations will be apparent to those skilled in theart in light of the foregoing description. Accordingly it is intended toembrace all such alternatives and modifications in variations as forwithin the spirit and broad scope of the appended claims.

1. An apparatus for identifying and quantifying components in aneffluent stream from an ammoxidation reactor, comprising: amicroprocessor; and a Fourier Transform infrared spectrometer having asample cell through which may flow a portion of said effluent stream, aninfrared source to emit infrared radiation and pass said infraredradiation through said effluent stream, an infrared detector to detecttransmitted infrared radiation at selected infrared wavelengths and togenerate absorbance data due to absorbance of said infrared radiation bysaid components, wherein each of said components absorbs infraredradiation at one or more of said infrared wavelengths, and an outputapparatus to provide said absorbance data to said microprocessor;wherein said microprocessor is programmed to identify and quantify eachof said plurality of components based upon said absorbance data andcalibration data, said calibration data being obtained from recovery runanalyses and calibration analyses in said sample cell.
 2. An apparatusas in claim 1, further comprising a memory device available to themicroprocessor for storing said calibration data for each of theplurality of components.
 3. An apparatus as in claim 1, furthercomprising an output device for outputting quantitative data for each ofsaid plurality of components.
 4. An apparatus as in claim 3, furthercomprising a reactor controller communicating with said ammoxidationreactor and said output device and adapted to adjust and controloperation of said ammoxidation reactor based on said quantitative data.5. An apparatus as in claim 4, wherein said reactor controller controlsone or more of reactor temperature, reactor internal pressure, feed ofair, feed of hydrocarbon and feed of ammonia.
 6. An apparatus as inclaim 5, wherein said reactor controller is controlled by saidmicroprocessor.
 7. An apparatus as in claim 1, further comprising adisplay for displaying data input to and output from saidmicroprocessor.
 8. An apparatus as in claim 2, wherein said calibrationdata provided to said memory device has been obtained from effluentsfrom said ammoxidation reactor.
 9. An apparatus as in claim 1, whereinsaid ammoxidation reactor is operated to produce acrylonitrile.
 10. Amethod for identifying and quantifying components in an effluent streamfrom an ammoxidation reactor, comprising: (A) advancing a portion ofsaid effluent stream through a sample cell in a Fourier Transforminfrared spectrometer; (B) scanning said portion in said sample cellwith infrared energy at a plurality of infrared wavelengths, whereineach of said components absorbs said infrared energy at one or more ofsaid plurality of selected wavelengths; (C) detecting said infraredradiation passing through said sample cell and generating absorbancedata for each of said components; and (D) quantifying each of saidcomponents by comparing said absorbance data to a calibration curve foreach component in a microprocessor programmed to quantify each of saidcomponents.
 11. A method as in claim 10, further comprising outputtingquantitative results for each of said plurality of components.
 12. Amethod as in claim 11, wherein said quantitative data is output to areactor controller communicating with said ammoxidation reactor and saidreactor controller is adapted to adjust and control operation of saidammoxidation reactor based on said quantitative data.
 13. A method as inclaim 12, wherein said reactor controller is controlled by saidmicroprocessor.
 14. A method as in claim 12, wherein said reactorcontroller controls one or more of reactor temperature, reactor internalpressure, feed of air, feed of hydrocarbon and feed of ammonia.
 15. Amethod as in claim 10, further comprising displaying data input to andoutput from said microprocessor.
 16. A method as in claim 10, whereinsaid calibration curve is calculated from calibration data obtained byperforming on effluents from said ammoxidation reactor recovery runanalyses and calibration analyses in said sample cell, said calibrationanalyses performed using steps (A), (B) and (C).
 17. A method as inclaim 10, wherein said ammoxidation reactor is operated to produceacrylonitrile.
 18. A method for controlling operation of an ammoxidationreactor based upon real-time quantitative analysis of components in aneffluent stream from said ammoxidation reactor, comprising: (a)preparing a calibration curve for each of said components by analyzing aplurality of effluent streams each containing said components by acalibration process comprising; (a-1) advancing at least a portion ofeach said effluent stream through a sample cell in a Fourier Transforminfrared spectrometer; (a-2) scanning each said effluent streamadvancing through said sample cell with infrared energy across a rangeof infrared wavelengths and obtaining absorbance data at selectedwavelengths across said range of infrared wavelengths; (a-3) collectingat least one sample corresponding to each said effluent stream; (a-4)performing a recovery run analysis on said at least one sample to obtainquantitative data for each of said components in said at least onesample; and (a-5) determining said calibration curve for each of saidcomponents by correlating said absorbance data and said quantitativedata; (b) obtaining real-time absorbance data for each of saidcomponents in an operational effluent from said ammoxidation reactor byperforming steps (a-1) and (a-2) thereon and calculating in amicroprocessor programmed therefor real-time quantitative data for saidoperational effluent from said calibration curve and said real-timeabsorbance data; and (c) controlling by a reactor controller saidammoxidation reactor to optimize production of at least one of saidcomponents based on said real-time quantitative data.
 19. A method as inclaim 18, wherein said calibration is performed in a microprocessorprogrammed to prepare said calibration curve.
 20. A method as in claim18, further comprising outputting quantitative results for each of saidplurality of components.
 21. A method as in claim 18, wherein saidquantitative data is output to a reactor controller communicating withsaid ammoxidation reactor and said reactor controller is adapted toadjust and control operation of said ammoxidation reactor based on saidquantitative data.
 22. A method as in claim 21, wherein saidammoxidation reactor controller is controlled by said microprocessor.23. A method as in claim 18, wherein said ammoxidation reactor isoperated to produce acrylonitrile.